Studying inter-core data reuse in multicores.

Most of existing research on emerging multicore machines focus on parallelism extraction and architectural level optimizations. While these optimizations are critical, complementary approaches such as data locality enhancement can also bring significant benefits. Most of the previous data locality optimization techniques have been proposed and evaluated in the context of single core architectures. While one can expect these optimizations to be useful for multicore machines as well, multicores present further opportunities due to shared on-chip caches most of them accommodate. In order to optimize data locality targeting multicore machines however, the first step is to understand data reuse characteristics of multithreaded applications and potential benefits shared caches can bring. Motivated by these observations, we make the following contributions in this paper. First, we give a definition for inter-core data reuse and quantify it on multicores using a set of ten multithreaded application programs. Second, we show that neither on-chip cache hierarchies of current multicore architectures nor state-of-the-art (single-core centric) code/data optimizations exploit available inter-core data reuse in multithreaded applications. Third, we demonstrate that exploiting all available intercore reuse could boost overall application performance by around 21.3% on average, indicating that there is significant scope for optimization. However, we also show that trying to optimize for inter-core reuse aggressively without considering the impact of doing so on intra-core reuse can actually perform worse than optimizing for intra-core reuse alone. Finally, we present a novel, compiler-based data locality optimization strategy for multicores that balances both inter-core and intra-core reuse optimizations carefully to maximize benefits that can be extracted from shared caches. Our experiments with this strategy reveal that it is very effective in optimizing data locality in multicores.